HOMICIDES - County Level
- summary of county homicide rates compared to state rate
## # A tibble: 2 x 4
## Higher Lower `No Diff` total
## <dbl> <dbl> <dbl> <dbl>
## 1 17 18 19 54
## 2 31.5 33.3 35.2 100
SUICIDES - County Level
- summary of county suicide rates compared to state rate
## # A tibble: 2 x 4
## Higher Lower `No Diff` total
## <dbl> <dbl> <dbl> <dbl>
## 1 27 5 26 58
## 2 46.6 8.62 44.8 100
HOMICIDES – INCREASES ONLY
SDOH Work
quick “exploration” of distributions of the three (plus) varibles we are “correlating”
histograms to look at the distribution of each
- community poverty and education from 2017 5-year ACS data, so covers 2013-2017; education is for population 25 and older
- community homicide is age-adjusted rate for 2013-2017 data combined, 39 (of 561) communities missing data, mostly do to cell suppression



observations:
- poverty - normal-ish, slight right skew (can’t be smaller than 0 or larger than 100)
- education - skewed left
- homicide - strong skew right
now looking at the “raw” associations

- appears to have strong linear association, but “spread” increasing with poverty (transform?)

- non-linear relationship - log-linear (i.e. exponential) explore transformation;
- explore difference in pattern between poverty and education with homicide
check that this is not a coding error with eduction by comapring “our” ACS data pull to HPI

- not a coding error (lack of exact correlation is from different years of data)
“added value” exploration with:
- linear modeling lines
- color based on CHANGES in homicide rates
- dot size based on number of homicides
- very messy pop-up to get county/community name

